As discussed before, data in source and target domain may vary in many aspects, for instance, in the case of HSIs, the dimensions of two HSIs may vary due to the acquisition from different sensors. Handling such cross-domain variations and transferring the knowledge between them is known as heterogeneous transfer learning (a detailed survey of such methods can be found in [1]}). In HSIC literature, several works have been proposed to bridge the gap for transferring the knowledge between two HSIs, with varying dimensions and/or distributions.
